| # Evaluation and reproduction |
|
|
| The primary benchmark scores the six released prediction files against the final |
| annotations for all 11,534 images. Use exact class identity for the main |
| comparison. Semantic relaxation and older experiments are explained in the |
| [supplementary guide](supplementary.md). |
|
|
| ## What counts as a correct prediction? |
|
|
| **Classification** compares the set of predicted classes with the gold set on |
| each image. A class present in both is a true positive (TP); an extra predicted |
| class is a false positive (FP); a missed gold class is a false negative (FN). |
| Repeated detections of the same class count once for classification. |
|
|
| **Localisation** compares individual boxes. A correct match requires the same |
| class and intersection-over-union (IoU) of at least 0.50. IoU is the area shared |
| by two boxes divided by their combined area. Maximum-cardinality matching finds |
| the largest possible set of one-to-one matches: neither box can be used twice. |
| Each match is a TP; unmatched predictions and gold boxes are FP and FN. |
|
|
| For example, suppose gold contains two litter boxes and one pothole box. A |
| prediction with two litter boxes and no pothole has classification TP=1 and |
| FN=1. If only one predicted litter box matches a gold box at the required IoU, |
| localisation has TP=1, FP=1 and FN=2. Predicting the right class does not by |
| itself localise every instance. |
|
|
| ## Metrics and populations |
|
|
| Precision is `TP / (TP + FP)` and recall is `TP / (TP + FN)`. The benchmark |
| emphasises recall through `F2 = 5TP / (5TP + FP + 4FN)`. Scores are reported on |
| a 0–1 scale. Micro metrics pool counts across classes before computing a score; |
| macro metrics average class-level scores over the classes represented in the |
| metric output. Domain micro scores pool only that domain's classes; averaging |
| domain scores does not recover the overall micro score. |
|
|
| A valid empty prediction contributes missed targets on a positive image and no |
| TP/FP/FN on a negative image. Correct negatives therefore do not inflate F2. The |
| separate negative-image false-positive rate measures the fraction of clean |
| images with at least one predicted violation. A malformed response is excluded |
| and counted as invalid, never treated as a correct negative. |
|
|
| `valid` scores use each model's valid images. `common` scores use their |
| intersection across the compared models. All six published runs have 11,534 |
| valid responses, so the two scopes coincide here. Always report coverage when |
| scoring another run; invalid or missing responses can change the population. |
|
|
| Evaluation retains every saved detection without confidence filtering, weighting |
| or ranking. Inference retention is separate: YOLO used `conf=0.10`, and VLMs |
| used no numerical confidence cutoff, although output-count caps can apply. These |
| metrics are not detection AP/mAP or ROC-AUC. Exact localisation at IoU 0.30 and |
| 0.40 is also supplied as sensitivity analysis. |
|
|
| See [model provenance](../evaluation/models/synthetic11534/README.md) for |
| recorded settings and limitations, including Gemini's box-axis interpretation. |
|
|
| ## Reproduce the published primary results |
|
|
| Download the full dataset snapshot, including the image files, and run these |
| commands from its root. Install dependencies in your existing Python |
| environment: |
|
|
| ```bash |
| python -m pip install -r code/requirements.txt |
| python examples/load_dataset.py --root . |
| python examples/render_annotation.py --root . --output /tmp/municipalbench.png |
| python examples/score_predictions.py --root . --output /tmp/model-scores.json |
| python examples/reproduce_agreement.py --root . --output /tmp/municipalbench-iaa |
| ``` |
|
|
| | Example | Successful result | |
| | --------- | -------------------------------------------------------------------------------------------- | |
| | Load | 11,534 unique image IDs, matching streaming and local loads | |
| | Render | An image with final gold boxes and class IDs at the chosen output path | |
| | Score | Exact classification/localisation summaries and per-class counts match both published scopes | |
| | Agreement | All first-pass groups, estimates and confidence intervals match | |
|
|
| Scoring uses the supplied predictions; it makes no model API calls. Agreement |
| reproduction uses 2,000 image-bootstrap replicates and can take several minutes. |
| Outputs are written outside the downloaded inventory. Preserve the snapshot and |
| record its HF commit SHA when reporting results. |
|
|
| `code/` contains the scoring and agreement implementation, with file hashes in |
| `code/source_inventory.json`. The examples verify primary scoring and agreement; |
| they do not rerun inference, recreate image generation, or replay historical |
| studies. The supplied semantic matrix and metric code support secondary |
| rescoring, but the scoring example above checks exact primary metrics only. |
|
|
